{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INST728E - Module 8. Sentiment Analysis\n",
    "\n",
    "A common task in social media analytics is sentiment analysis, where we measure how positive or negative a message is by its text. Note that we often omit emoji despite their potential utility. Recent research is working to integrate that information.\n",
    "\n",
    "For now though, we'll look into sentiment towards our event over time.\n",
    "\n",
    "For that, we'll use the temporal methods from Module 5 and introduce some new packages for sentiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NLTK's sentiment code\n",
    "import nltk\n",
    "import nltk.sentiment.util\n",
    "import nltk.sentiment.vader\n",
    "\n",
    "# TextBlob provides its own sentiment analysis\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carry Forward Disaster Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crisisInfo = {\n",
    "    \"Women's March\": {\n",
    "        \"name\": \"Women's March 2017\",\n",
    "        \"time\": 1484992800, # 21 January 2017, 6:58 UTC to 08:11 UTC\n",
    "        \"directory\": \"womensmarch\",    # Where do we find the relevant files\n",
    "        \"keywords\": [    # How can we describe this event?\n",
    "            \"women's march\",\"resist\", \"notmypresident\",\"inauguration\",\"women's right\",\"human right\",\"planned parenthood\"\n",
    "        ],\n",
    "        \"place\": [\n",
    "            38.899539,# Latitude\n",
    "            -77.036551 # Longitude\n",
    "        ],\n",
    "        \"box\": {    # Where did this event occur?\n",
    "            \"lowerLeftLon\": -77.119759,\n",
    "            \"lowerLeftLat\": 38.791645,\n",
    "            \"upperRightLon\": -76.909393,\n",
    "            \"upperRightLat\": 38.995548,\n",
    "        }\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replace the name below with your selected crisis\n",
    "selectedCrisis = \"Women's March\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Reading Relevant Tweets\n",
    "\n",
    "Re-read our relevant tweets..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relevant Tweets: 4447\n"
     ]
    }
   ],
   "source": [
    "\n",
    "in_file_path = \"/Users/yutingliao/Desktop/INST728 E/relevant_tweet_output_keywords_updated.json\" # Replace this as necessary\n",
    "\n",
    "relevant_tweets = []\n",
    "with open(in_file_path, \"r\") as in_file:\n",
    "    for line in in_file:\n",
    "        relevant_tweets.append(json.loads(line.encode(\"utf8\")))\n",
    "        \n",
    "print(\"Relevant Tweets:\", len(relevant_tweets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporal Ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n",
      "Start Time: 2017-01-21 10:00:00\n",
      "Stop Time: 2017-01-24 23:00:00\n",
      "Processed Times: 5101\n"
     ]
    }
   ],
   "source": [
    "# Twitter's time format, for parsing the created_at date\n",
    "timeFormat = \"%a %b %d %H:%M:%S +0000 %Y\"\n",
    "\n",
    "# Frequency map for tweet-times\n",
    "rel_frequency_map = {}\n",
    "for tweet in relevant_tweets:\n",
    "    # Parse time\n",
    "    currentTime = datetime.datetime.strptime(tweet['created_at'], timeFormat)\n",
    "\n",
    "    # Flatten this tweet's time\n",
    "    currentTime = currentTime.replace(second=0, minute = 0)\n",
    "\n",
    "    # If our frequency map already has this time, use it, otherwise add\n",
    "    extended_list = rel_frequency_map.get(currentTime, [])\n",
    "    extended_list.append(tweet)\n",
    "    rel_frequency_map[currentTime] = extended_list\n",
    "    \n",
    "# Fill in any gaps\n",
    "times = sorted(rel_frequency_map.keys())\n",
    "print (len(times))\n",
    "firstTime = times[0]\n",
    "lastTime = times[-1]\n",
    "thisTime = firstTime\n",
    "\n",
    "# We want to look at per-minute data, so we fill in any missing minutes\n",
    "timeIntervalStep = datetime.timedelta(0, 60)    # Time step in seconds\n",
    "while ( thisTime <= lastTime ):\n",
    "\n",
    "    rel_frequency_map[thisTime] = rel_frequency_map.get(thisTime, [])\n",
    "        \n",
    "    thisTime = thisTime + timeIntervalStep\n",
    "\n",
    "# Count the number of minutes\n",
    "print (\"Start Time:\", firstTime)\n",
    "print (\"Stop Time:\", lastTime)\n",
    "print (\"Processed Times:\", len(rel_frequency_map))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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JAACAxEgiAQAAkNiwSaRz7jLn3LPOuTkNw851zi1yzt1X+/eWhu++5Jyb55x7xDl3XFaB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\nkZ111llnnXXWWWedNbb/D/+2IVPXyBB+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23986d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Tweet Frequencies\")\n",
    "\n",
    "sortedTimes = sorted(rel_frequency_map.keys())\n",
    "postFreqList = [len(rel_frequency_map[x]) for x in sortedTimes]\n",
    "\n",
    "smallerXTicks = range(0, len(sortedTimes), 90)\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "ax.plot(xData, postFreqList, color=\"blue\", label=\"Posts\")\n",
    "\n",
    "ax.grid(b=True, which=u'major')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Content and Sentiment Analysis\n",
    "\n",
    "\"Sentiment analysis\" is used to figure out how people **feel** about a specific topic.\n",
    "Some tools also provide measurements like subjectivity/objectivity of text content.\n",
    "\n",
    "We'll cover:\n",
    "\n",
    "- Topically Relevant Filtering\n",
    "- Sentiment, Subjectivity, and Objectivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment Analysis w/ TextBlob\n",
    "\n",
    "TextBlob is a nice Python package that provides a number of useful text processing capabilities.\n",
    "We will use it for sentiment analysis to calculate polarity and subjectivity for each relevant tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3qe1X0iq3vApDxZIkR2o7IJJ7eruBxd8vfBJCIFHBiUSsg4ocw2pBxRIAXv4d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Dpfd/sg6VJuW/7/lcCStvVPAjYC9/ah+13gqR1sHgL8i5fNmuzOqPOwmboOeAmwV4d4HQ68s/B5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\nyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a230c6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sentiment values\n",
    "polarVals_tb = []\n",
    "objVals = []\n",
    "\n",
    "# For each minute, pull the tweet text and search for the keywords we want\n",
    "for t in sortedTimes:\n",
    "    tweets = rel_frequency_map[t]\n",
    "    \n",
    "    # For calculating averages\n",
    "    localPolarVals = []\n",
    "    localObjVals = []\n",
    "    \n",
    "    for tweet in tweets:\n",
    "        tweetString = tweet[\"text\"]\n",
    "\n",
    "        blob = TextBlob(tweetString)\n",
    "        polarity = blob.sentiment.polarity\n",
    "        objectivity = blob.sentiment.subjectivity\n",
    "        \n",
    "        localPolarVals.append(polarity)\n",
    "        localObjVals.append(objectivity)\n",
    "        \n",
    "    # Add data to the polarity and objectivity measure arrays\n",
    "    if ( len(tweets) > 0 ):\n",
    "        polarVals_tb.append(np.mean(localPolarVals))\n",
    "        objVals.append(np.mean(localObjVals))\n",
    "    else:\n",
    "        polarVals_tb.append(0.0)\n",
    "        objVals.append(0.0)\n",
    "\n",
    "        \n",
    "# Now plot this sentiment data\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Sentiment\")\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "# Polarity is scaled [-1, 1], for negative and positive polarity\n",
    "ax.plot(xData, polarVals_tb, label=\"Polarity\")\n",
    "\n",
    "# Subjetivity is scaled [0, 1], with 0 = objective, 1 = subjective\n",
    "ax.plot(xData, objVals, label=\"Subjectivity\")\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(b=True, which=u'major')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentiment Analysis with Vader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vader = nltk.sentiment.vader.SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mGhO3re3T2Xwfw1bOFhBqpnPz9l3avqvWrG7ymEBjuPwhFvscsLF06owOsb+o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\n5iKNk6brVqWZpiF11sOCLG1lzK3yMTOrYh9vob1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cEZFtgceGjXcR2TeEcKVBWFUmWCap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\nZcFXMg3rm/oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a238e0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sentiment values\n",
    "polarVals_vader = []\n",
    "\n",
    "# For each minute, pull the tweet text and search for the keywords we want\n",
    "for t in sortedTimes:\n",
    "    tweets = rel_frequency_map[t]\n",
    "    \n",
    "    # For calculating averages\n",
    "    localPolarVals = []\n",
    "    \n",
    "    for tweet in tweets:\n",
    "        tweetString = tweet[\"text\"]\n",
    "\n",
    "        polarity = vader.polarity_scores(tweetString)[\"compound\"]\n",
    "        \n",
    "        localPolarVals.append(polarity)\n",
    "        \n",
    "    # Add data to the polarity and objectivity measure arrays\n",
    "    if ( len(tweets) > 0 ):\n",
    "        polarVals_vader.append(np.mean(localPolarVals))\n",
    "    else:\n",
    "        polarVals_vader.append(0.0)\n",
    "\n",
    "        \n",
    "# Now plot this sentiment data\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Sentiment\")\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "# Polarity is scaled [-1, 1], for negative and positive polarity\n",
    "ax.plot(xData, polarVals_vader, label=\"Polarity\")\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(b=True, which=u'major')\n",
    "\n",
    "plt.ylim((-0.95, 0.95))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vader vs. TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eR9n1p/OgWITOEGZB3Cz6Gokxr+yt53TiY2dIbasqXmRHd+YO4n2BhBQXaYeT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\nzuL0PDYEMyCLdUBsXKrhG18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ce9Aees8+w/S9jx1acNtVnzlSf3761TpUVZ1EQucuadBQ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\n7Pb/AVz5sREENtd7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a238d6898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now plot this sentiment data\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8.5)\n",
    "\n",
    "plt.title(\"Sentiment\")\n",
    "plt.xticks(smallerXTicks, [sortedTimes[x] for x in smallerXTicks], rotation=90)\n",
    "\n",
    "xData = range(len(sortedTimes))\n",
    "\n",
    "# Polarity is scaled [-1, 1], for negative and positive polarity\n",
    "ax.plot(xData, polarVals_vader, label=\"VADER\")\n",
    "ax.plot(xData, polarVals_tb, label=\"TextBlob\")\n",
    "\n",
    "ax.legend()\n",
    "ax.grid(b=True, which=u'major')\n",
    "\n",
    "plt.ylim((-0.95, 0.95))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sentiment Extremes\n",
    "\n",
    "While sentiment analysis may seem a little silly given our focus on disaster, for some types of events, content that is especially negative or positive is interesting. \n",
    "\n",
    "For example, who might be posting extremely positive tweets in the wake of a terrorist attack?\n",
    "\n",
    "To this end, let us use VADER to find the __top 5__ most positive and negative tweets in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_k = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate sentiment for each relevant tweet\n",
    "sentiment_pairs = [(tweet, vader.polarity_scores(tweet[\"text\"])[\"compound\"]) \n",
    "                   for tweet in relevant_tweets]\n",
    "\n",
    "sorted_tweets = sorted(sentiment_pairs, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: PZankl Sentiment: -0.9477\n",
      "Text:\n",
      "RT @rtraister: No reason to do this except a fear of the growing resistance to this cruel, unjust, racist, sexist law.  https://t.co/sP4oHL… \n",
      "\n",
      "Author: d3d0c3d Sentiment: -0.9231\n",
      "Text:\n",
      "@mehreenkasana @Anony_Mia keep spreading your message of hatred violating basic human rights, that moment in time he posed no threat. #wtf \n",
      "\n",
      "Author: warpony2310 Sentiment: -0.9201\n",
      "Text:\n",
      "Joe's Crime Blog/Human Right's Site: Two Suspect Terrorists Commit Suicide after Shootout in Saudi Arabia... https://t.co/oR8H6GRCLz \n",
      "\n",
      "Author: 3than_A Sentiment: -0.9118\n",
      "Text:\n",
      "RT @CurveResistant: Nah no way, my wife better let us all die. The kids, us, the dog, and the neighbors fuck you mean this bitch is a h…  \n",
      "\n",
      "Author: iconcIit Sentiment: -0.9106\n",
      "Text:\n",
      "She's marching for women's rights and you decide to comment on what she's wearing. Fucking stupid ass bitch. https://t.co/wjSjA1xDcB \n",
      "\n",
      "Author: Guardian_Elite Sentiment: -0.9096\n",
      "Text:\n",
      "RT @Notmanipulated7: #ResistFromDay1 bc nothing trumps love, like hate &amp; a fat George Soros paycheck 💁🏼 \n",
      "\n",
      "(These riots are sooo \"organic…  \n",
      "\n",
      "Author: UkNarayan77 Sentiment: -0.8964\n",
      "Text:\n",
      "@pandit1010 @NeelamKapur \n",
      "3+Yrs in jail\n",
      "Medical/FIR-  No Rape\n",
      "is this Humanity?\n",
      "Human Right??\n",
      "Shame on System!… https://t.co/8XYcmCZ64N \n",
      "\n",
      "Author: Symph_ony Sentiment: -0.8941\n",
      "Text:\n",
      "RT @tariqnasheed: Louisiana has made it a federal \"hate crime\" to resist arrest. Understand, just QUESTIONING a cop is considered \"resistin… \n",
      "\n",
      "Author: darianjulun Sentiment: -0.8941\n",
      "Text:\n",
      "RT @tariqnasheed: Louisiana has made it a federal \"hate crime\" to resist arrest. Understand, just QUESTIONING a cop is considered \"resistin… \n",
      "\n",
      "Author: AustinEir1 Sentiment: -0.8934\n",
      "Text:\n",
      "Go to a damn news station.\n",
      "\n",
      "Ignore these liars. They want to watch the USA destruct.\n",
      "\n",
      "#womensmarch #theResistance https://t.co/ieMcReQG6s \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Most negative tweets\n",
    "for tweet, sentiment in sorted_tweets[:top_k]:\n",
    "    print(\"Author:\", tweet[\"user\"][\"screen_name\"], \"Sentiment:\", sentiment)\n",
    "    print(\"Text:\\n%s\" % tweet[\"text\"], \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: hackerkween Sentiment: 0.9217\n",
      "Text:\n",
      "RT @machinegunkelly: I'm super proud of my all my friends walking in the women's march today. also proud of whoever punched Richard Spencer… \n",
      "\n",
      "Author: RedXPower Sentiment: 0.9217\n",
      "Text:\n",
      "RT @varadmehta: \"Resistance,\" on the other hand, is noble, heroic. It resounds of brave, self-sacrificing Frenchmen standing against the Na… \n",
      "\n",
      "Author: Colodemvoter Sentiment: 0.9274\n",
      "Text:\n",
      "@elizabethforma @SharpShooterSev -\"A woman's place is in the Resistance\" -great updated slogan. Truly hope that SecClinton is happy \n",
      "today. \n",
      "\n",
      "Author: h0700k_23 Sentiment: 0.9274\n",
      "Text:\n",
      "RT @rosandraafierro: Love is love. Black lives matter. Climate change is real. Immigrants make American great. Women's rights are Human…  \n",
      "\n",
      "Author: crackleackles Sentiment: 0.9274\n",
      "Text:\n",
      "RT @samtruglio: \"Love is love. Black lives matter. Immigrants make America great. Climate change is real. Women's rights are human rights\" \n",
      "\n",
      "Author: Christi21689609 Sentiment: 0.9515\n",
      "Text:\n",
      "RT @MarkRuffalo: Thank you dear women of our lives!  Thank you for your wisdom, courage, and strength! #100daysofresistance #WomensMarch \n",
      "\n",
      "Author: lieztche Sentiment: 0.9515\n",
      "Text:\n",
      "RT @MarkRuffalo: Thank you dear women of our lives!  Thank you for your wisdom, courage, and strength! #100daysofresistance #WomensMarch \n",
      "\n",
      "Author: _xamsassadors Sentiment: 0.9538\n",
      "Text:\n",
      "RT @waIkngdeniaI: daily i love kat tweet: hi kat i love u i hope u had a good time at the women's march today i'm proud of u \n",
      "\n",
      "Author: PaulRossPR Sentiment: 0.9565\n",
      "Text:\n",
      "RT @patersonderek18: Good morning everyone hope you all had a great night good luck to everyone on women's march keep safe https://t.co/eOb… \n",
      "\n",
      "Author: L3ssthanaverage Sentiment: 0.9625\n",
      "Text:\n",
      "Wish so much I could be at #WomensMarch but I wish all of the strong and brave women there luck! #ResistTrump \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Most negative tweets\n",
    "for tweet, sentiment in sorted_tweets[-top_k:]:\n",
    "    print(\"Author:\", tweet[\"user\"][\"screen_name\"], \"Sentiment:\", sentiment)\n",
    "    print(\"Text:\\n%s\" % tweet[\"text\"], \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compared to TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Negative Tweets:\n",
      "Author: MellieMel57 Sentiment: -1.0\n",
      "Text:\n",
      "Canadians traveling to Women's March denied US entry after sharing plans https://t.co/k58k0A2h2f  The Northern Wall is Up Disgusting \n",
      "\n",
      "Author: AdsGriffs Sentiment: -1.0\n",
      "Text:\n",
      "@ReliableSources get some fucking people of color on there!!!!!! It's 2017, the women's march was yesterday, GET IT TOGETHER. \n",
      "\n",
      "Author: DanielaMontiel Sentiment: -1.0\n",
      "Text:\n",
      "RT @MRodOfficial: https://t.co/7lAMXqmZ6J women's march Ashley Judd speech. I'm a nasty women too I stand with all you gals to the death, A… \n",
      "\n",
      "Author: isleswithsnow Sentiment: -1.0\n",
      "Text:\n",
      "RT @GOPBlackChick: Women's March=bunch of nasty liberal women. https://t.co/gE7uGiq71W \n",
      "\n",
      "Author: OnyianiE Sentiment: -1.0\n",
      "Text:\n",
      "RT @newslaves2016: Absolutely HORRIBLE. Biafrans celebrating trump are massacred!!! \n",
      "\n",
      "#MAGA #inauguration #protest #resistance…  \n",
      "\n",
      "Author: Yes_WeThePeople Sentiment: -1.0\n",
      "Text:\n",
      "#ResistTrumpTuesdays  Filthy liberals--get a job and go to work instead of mooching on the taxpayers! \n",
      "\n",
      "Author: notalegitpotus Sentiment: -1.0\n",
      "Text:\n",
      "@therealezway @funder outrageous! #SwampCabinet #resisttrump @MoveOn  #Indivisible \n",
      "\n",
      "Author: ZachGasper Sentiment: -1.0\n",
      "Text:\n",
      "RT @billyeichner: Well this is insane. Our President is a pathological liar and a sociopath. #Resist https://t.co/TL379ZqjPA \n",
      "\n",
      "Author: Kaulitzophile Sentiment: -1.0\n",
      "Text:\n",
      "RT @billyeichner: Well this is insane. Our President is a pathological liar and a sociopath. #Resist https://t.co/TL379ZqjPA \n",
      "\n",
      "Author: saintcoral Sentiment: -1.0\n",
      "Text:\n",
      "RT @newslaves2016: Absolutely HORRIBLE. Biafrans celebrating trump are massacred!!! \n",
      "\n",
      "#MAGA #inauguration #protest #resistance…  \n",
      "\n",
      "------------------------\n",
      "Most Positive Tweets:\n",
      "Author: scroeser Sentiment: 1.0\n",
      "Text:\n",
      "Our best self defense is resistance that crosses borders, literal &amp; metaphoric. https://t.co/CcqOGAj1TJ \n",
      "\n",
      "Author: eddyrogerparker Sentiment: 1.0\n",
      "Text:\n",
      "RT @BrokenLiberal: Joy! This is the power of #TheResistance ! https://t.co/2DvXJ8aPzC \n",
      "\n",
      "Author: caroljdavy Sentiment: 1.0\n",
      "Text:\n",
      "RT @queertardo: #TheResistance\n",
      "There's some EXCELLENT INFO in this THREAD... \n",
      "https://t.co/WcIy2xZOcH \n",
      "\n",
      "Author: jguenter13 Sentiment: 1.0\n",
      "Text:\n",
      "RT @bruneski: Are u an awesome organizer? Want to work with the @SierraClub to #resist &amp; fight for climate justice? Apply here https://t.co… \n",
      "\n",
      "Author: RightInTheDesu Sentiment: 1.0\n",
      "Text:\n",
      "RT @gabrielasexy20: You can resist me?? 👄👄👄 #mondaymotivation @TheHODSquad ☎️02035890125 in #west #Kensington  retweet to #win me!!! 💋💋💋 ht… \n",
      "\n",
      "Author: LteukRain Sentiment: 1.0\n",
      "Text:\n",
      "RT @Minsera: #이민우 #M cannot resist.. TT so cute ahhhhhh!!!!! https://t.co/Dx23LMReXE \n",
      "\n",
      "Author: helenapatrikiou Sentiment: 1.0\n",
      "Text:\n",
      "@BadHombreNPS Follow this brave account. Resist against censorship! \n",
      "\n",
      "Author: sherrilevy Sentiment: 1.0\n",
      "Text:\n",
      "@realDonaldTrump marching against you was one of the greatest joys of my life. #notmypresident \n",
      "\n",
      "Author: SarahLume Sentiment: 1.0\n",
      "Text:\n",
      "@realDonaldTrump  yeah, you don't agree on Women's rights ! Great news !! \n",
      "\n",
      "Author: gianitals Sentiment: 1.0\n",
      "Text:\n",
      "RT @breebunn: My friend designed these awesome pussy pins and all proceeds go to Planned Parenthood if you wanna get one…  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate sentiment for each relevant tweet\n",
    "sentiment_pairs = [(tweet, TextBlob(tweet[\"text\"]).sentiment.polarity) \n",
    "                   for tweet in relevant_tweets]\n",
    "\n",
    "sorted_tweets = sorted(sentiment_pairs, key=lambda x: x[1])\n",
    "\n",
    "print(\"Most Negative Tweets:\")\n",
    "# Most negative tweets\n",
    "for tweet, sentiment in sorted_tweets[:top_k]:\n",
    "    print(\"Author:\", tweet[\"user\"][\"screen_name\"], \"Sentiment:\", sentiment)\n",
    "    print(\"Text:\\n%s\" % tweet[\"text\"], \"\\n\")\n",
    "    \n",
    "print(\"------------------------\")\n",
    "print(\"Most Positive Tweets:\")\n",
    "# Most positive tweets\n",
    "for tweet, sentiment in sorted_tweets[-top_k:]:\n",
    "    print(\"Author:\", tweet[\"user\"][\"screen_name\"], \"Sentiment:\", sentiment)\n",
    "    print(\"Text:\\n%s\" % tweet[\"text\"], \"\\n\")"
   ]
  }
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